Time domain characterization of nonstationary low-Mach number aeroacoustic sources using principal component analysis.

This paper presents the use of principal component analysis (PCA) for time domain microphone array denoising to characterize an impulsive aeroacoustic source, which is illustrated with the aeroacoustic emission caused by a vortex ring/edge interaction. Prior studies have used signal processing approaches that required assumptions about the source directivity or user intervention at low signal-to-noise ratio (SNR) conditions. In this context, PCA, a matrix decomposition tool which identifies the most common features across an ensemble of observations, provides a data-driven (hands-off) approach to signal processing.